{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Machine Learning for Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Notebook for Session06: Advanced Features  - Feature Engineering and Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Import titanic data using pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1313 entries, 0 to 1312\n",
      "Data columns (total 11 columns):\n",
      "row.names    1313  non-null values\n",
      "pclass       1313  non-null values\n",
      "survived     1313  non-null values\n",
      "name         1313  non-null values\n",
      "age          633  non-null values\n",
      "embarked     821  non-null values\n",
      "home.dest    754  non-null values\n",
      "room         77  non-null values\n",
      "ticket       69  non-null values\n",
      "boat         347  non-null values\n",
      "sex          1313  non-null values\n",
      "dtypes: float64(1), int64(2), object(8)\n"
     ]
    }
   ],
   "source": [
    "titanic = pd.read_csv('data/titanic.csv')\n",
    "print titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  pclass  survived      age     embarked   boat     sex\n",
      "0    1st         1  29.0000  Southampton      2  female\n",
      "1    1st         0   2.0000  Southampton    NaN  female\n",
      "2    1st         0  30.0000  Southampton  (135)    male\n",
      "3    1st         0  25.0000  Southampton    NaN  female\n",
      "4    1st         1   0.9167  Southampton     11    male\n"
     ]
    }
   ],
   "source": [
    "print titanic.head()[['pclass', 'survived', 'age', 'embarked', 'boat', 'sex']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row.names</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 633.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  657.000000</td>\n",
       "      <td>    0.341965</td>\n",
       "      <td>  31.194181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  379.174762</td>\n",
       "      <td>    0.474549</td>\n",
       "      <td>  14.747525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>   0.166700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>  329.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>  21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>  657.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>  30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>  985.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>  41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 1313.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>  71.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         row.names     survived         age\n",
       "count  1313.000000  1313.000000  633.000000\n",
       "mean    657.000000     0.341965   31.194181\n",
       "std     379.174762     0.474549   14.747525\n",
       "min       1.000000     0.000000    0.166700\n",
       "25%     329.000000     0.000000   21.000000\n",
       "50%     657.000000     0.000000   30.000000\n",
       "75%     985.000000     1.000000   41.000000\n",
       "max    1313.000000     1.000000   71.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Feature extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Convert categorical features to 1-of-k using _DictVectorizer()_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import feature_extraction\n",
    "\n",
    "def one_hot_dataframe(data, cols, replace=False):\n",
    "    vec = feature_extraction.DictVectorizer()\n",
    "    mkdict = lambda row: dict((col, row[col]) for col in cols)\n",
    "    vecData = pd.DataFrame(vec.fit_transform(data[cols].apply(mkdict, axis=1)).toarray())\n",
    "    vecData.columns = vec.get_feature_names()\n",
    "    vecData.index = data.index\n",
    "    if replace:\n",
    "        data = data.drop(cols, axis=1)\n",
    "        data = data.join(vecData)\n",
    "    return (data, vecData)\n",
    "\n",
    "titanic, titanic_n = one_hot_dataframe(titanic, ['pclass', 'embarked', 'sex'], replace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>row.names</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>embarked</th>\n",
       "      <th>embarked=Cherbourg</th>\n",
       "      <th>embarked=Queenstown</th>\n",
       "      <th>embarked=Southampton</th>\n",
       "      <th>pclass=1st</th>\n",
       "      <th>pclass=2nd</th>\n",
       "      <th>pclass=3rd</th>\n",
       "      <th>sex=female</th>\n",
       "      <th>sex=male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 633.000000</td>\n",
       "      <td> 821</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "      <td> 1313.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>  657.000000</td>\n",
       "      <td>    0.341965</td>\n",
       "      <td>  31.194181</td>\n",
       "      <td>   0</td>\n",
       "      <td>    0.154608</td>\n",
       "      <td>    0.034273</td>\n",
       "      <td>    0.436405</td>\n",
       "      <td>    0.245240</td>\n",
       "      <td>    0.213252</td>\n",
       "      <td>    0.541508</td>\n",
       "      <td>    0.352628</td>\n",
       "      <td>    0.647372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>  379.174762</td>\n",
       "      <td>    0.474549</td>\n",
       "      <td>  14.747525</td>\n",
       "      <td>   0</td>\n",
       "      <td>    0.361668</td>\n",
       "      <td>    0.181998</td>\n",
       "      <td>    0.496128</td>\n",
       "      <td>    0.430393</td>\n",
       "      <td>    0.409760</td>\n",
       "      <td>    0.498464</td>\n",
       "      <td>    0.477970</td>\n",
       "      <td>    0.477970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>   0.166700</td>\n",
       "      <td>   0</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>  329.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>  21.000000</td>\n",
       "      <td>   0</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>  657.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>  30.000000</td>\n",
       "      <td>   0</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>  985.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>  41.000000</td>\n",
       "      <td>   0</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    0.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td> 1313.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>  71.000000</td>\n",
       "      <td>   0</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "      <td>    1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         row.names     survived         age  embarked  embarked=Cherbourg  \\\n",
       "count  1313.000000  1313.000000  633.000000       821         1313.000000   \n",
       "mean    657.000000     0.341965   31.194181         0            0.154608   \n",
       "std     379.174762     0.474549   14.747525         0            0.361668   \n",
       "min       1.000000     0.000000    0.166700         0            0.000000   \n",
       "25%     329.000000     0.000000   21.000000         0            0.000000   \n",
       "50%     657.000000     0.000000   30.000000         0            0.000000   \n",
       "75%     985.000000     1.000000   41.000000         0            0.000000   \n",
       "max    1313.000000     1.000000   71.000000         0            1.000000   \n",
       "\n",
       "       embarked=Queenstown  embarked=Southampton   pclass=1st   pclass=2nd  \\\n",
       "count          1313.000000           1313.000000  1313.000000  1313.000000   \n",
       "mean              0.034273              0.436405     0.245240     0.213252   \n",
       "std               0.181998              0.496128     0.430393     0.409760   \n",
       "min               0.000000              0.000000     0.000000     0.000000   \n",
       "25%               0.000000              0.000000     0.000000     0.000000   \n",
       "50%               0.000000              0.000000     0.000000     0.000000   \n",
       "75%               0.000000              1.000000     0.000000     0.000000   \n",
       "max               1.000000              1.000000     1.000000     1.000000   \n",
       "\n",
       "        pclass=3rd   sex=female     sex=male  \n",
       "count  1313.000000  1313.000000  1313.000000  \n",
       "mean      0.541508     0.352628     0.647372  \n",
       "std       0.498464     0.477970     0.477970  \n",
       "min       0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000  \n",
       "50%       1.000000     0.000000     1.000000  \n",
       "75%       1.000000     1.000000     1.000000  \n",
       "max       1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "What does the 'embarked' feature has?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   embarked  embarked=Cherbourg  embarked=Queenstown  embarked=Southampton  \\\n",
      "0         0                   0                    0                     1   \n",
      "1         0                   0                    0                     1   \n",
      "2         0                   0                    0                     1   \n",
      "3         0                   0                    0                     1   \n",
      "4         0                   0                    0                     1   \n",
      "\n",
      "   pclass=1st  pclass=2nd  pclass=3rd  sex=female  sex=male  \n",
      "0           1           0           0           1         0  \n",
      "1           1           0           0           1         0  \n",
      "2           1           0           0           0         1  \n",
      "3           1           0           0           1         0  \n",
      "4           1           0           0           0         1  \n",
      "     embarked  embarked=Cherbourg  embarked=Queenstown  embarked=Southampton  \\\n",
      "62        NaN                   0                    0                     0   \n",
      "165       NaN                   0                    0                     0   \n",
      "195       NaN                   0                    0                     0   \n",
      "196       NaN                   0                    0                     0   \n",
      "229       NaN                   0                    0                     0   \n",
      "\n",
      "     pclass=1st  pclass=2nd  pclass=3rd  sex=female  sex=male  \n",
      "62            1           0           0           0         1  \n",
      "165           1           0           0           0         1  \n",
      "195           1           0           0           0         1  \n",
      "196           1           0           0           0         1  \n",
      "229           1           0           0           0         1  \n"
     ]
    }
   ],
   "source": [
    "print titanic_n.head(5)\n",
    "print titanic_n[titanic_n['embarked'] != 0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Convert the remaining categorical features..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   row.names  survived                                             name  \\\n",
      "0          1         1                     Allen, Miss Elisabeth Walton   \n",
      "1          2         0                      Allison, Miss Helen Loraine   \n",
      "2          3         0              Allison, Mr Hudson Joshua Creighton   \n",
      "3          4         0  Allison, Mrs Hudson J.C. (Bessie Waldo Daniels)   \n",
      "4          5         1                    Allison, Master Hudson Trevor   \n",
      "\n",
      "       age                        home.dest room      ticket   boat  embarked  \\\n",
      "0  29.0000                     St Louis, MO  B-5  24160 L221      2         0   \n",
      "1   2.0000  Montreal, PQ / Chesterville, ON  C26         NaN    NaN         0   \n",
      "2  30.0000  Montreal, PQ / Chesterville, ON  C26         NaN  (135)         0   \n",
      "3  25.0000  Montreal, PQ / Chesterville, ON  C26         NaN    NaN         0   \n",
      "4   0.9167  Montreal, PQ / Chesterville, ON  C22         NaN     11         0   \n",
      "\n",
      "   embarked=Cherbourg  embarked=Queenstown  embarked=Southampton  pclass=1st  \\\n",
      "0                   0                    0                     1           1   \n",
      "1                   0                    0                     1           1   \n",
      "2                   0                    0                     1           1   \n",
      "3                   0                    0                     1           1   \n",
      "4                   0                    0                     1           1   \n",
      "\n",
      "   pclass=2nd  pclass=3rd  sex=female  sex=male  \n",
      "0           0           0           1         0  \n",
      "1           0           0           1         0  \n",
      "2           0           0           0         1  \n",
      "3           0           0           1         0  \n",
      "4           0           0           0         1  \n"
     ]
    }
   ],
   "source": [
    "print titanic.head()\n",
    "titanic, titanic_n = one_hot_dataframe(titanic, ['home.dest', 'room', 'ticket', 'boat'], replace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Adjust N/A ages with the mean age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    633.000000\n",
      "mean      31.194181\n",
      "std       14.747525\n",
      "min        0.166700\n",
      "25%       21.000000\n",
      "50%       30.000000\n",
      "75%       41.000000\n",
      "max       71.000000\n",
      "dtype: float64\n",
      "count    1313.000000\n",
      "mean       31.194181\n",
      "std        10.235540\n",
      "min         0.166700\n",
      "25%        30.000000\n",
      "50%        31.194181\n",
      "75%        31.194181\n",
      "max        71.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print titanic['age'].describe()\n",
    "mean = titanic['age'].mean()\n",
    "titanic['age'].fillna(mean, inplace=True)\n",
    "print titanic['age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Complete n/a with zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "titanic.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1313 entries, 0 to 1312\n",
      "Columns: 581 entries, row.names to ticket=L15 1s\n",
      "dtypes: float64(578), int64(2), object(1)\n"
     ]
    }
   ],
   "source": [
    "print titanic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Build the training and testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "titanic_target = titanic['survived']\n",
    "titanic_data = titanic.drop(['name', 'row.names', 'survived'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(titanic_data, titanic_target, test_size=0.25, random_state=33)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "dt = tree.DecisionTreeClassifier(criterion='entropy')\n",
    "dt = dt.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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yEhIYMGDeIIAgAwvKlTp9ra2k6dOlV2kDyhZs2aJ0+e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\nIJs0Gs3gwYPDwsKmTZuWYtHDhw+9vLxq16595syZ9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RXHjx/XfghTfzPAuLkF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ru7u9wp7IVKpQoMDDx//vxXX32VkpLStWtXPz+/3377Te5cAAAYDWNU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\nzsydO5cBKiYSGBjo6OhY6Z+USqW/v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SXu4lR/Kx3o01322QtdRUWFiERFRXk9PkaJiYnHjx/3qeZglfEslPNSKlc/labu7o2z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\nH4rS0tKpU6c2atQoN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      "text/plain": [
       "<IPython.core.display.Image at 0x68f4fd0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydot, StringIO\n",
    "dot_data = StringIO.StringIO()\n",
    "tree.export_graphviz(dt, out_file=dot_data, feature_names=titanic_data.columns)\n",
    "graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png('titanic.png')\n",
    "from IPython.core.display import Image\n",
    "Image(filename='titanic.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "def measure_performance(X, y, clf, show_accuracy=True, show_classification_report=True, show_confussion_matrix=True):\n",
    "    y_pred = clf.predict(X)   \n",
    "    if show_accuracy:\n",
    "         print \"Accuracy:{0:.3f}\".format(metrics.accuracy_score(y, y_pred)),\"\\n\"\n",
    "    if show_classification_report:\n",
    "        print \"Classification report\"\n",
    "        print metrics.classification_report(y, y_pred),\"\\n\"\n",
    "      \n",
    "    if show_confussion_matrix:\n",
    "        print \"Confussion matrix\"\n",
    "        print metrics.confusion_matrix(y, y_pred),\"\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.827 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "measure_performance(X_test, y_test, dt, show_confussion_matrix=False, show_classification_report=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Select only the 20% most important features, using a chi2 test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'age', u'embarked=Cherbourg', u'embarked=Southampton', u'pclass=1st', u'pclass=2nd', u'pclass=3rd', u'sex=female', u'sex=male', u'boat=1', u'boat=10', u'boat=11', u'boat=12', u'boat=13', u'boat=14', u'boat=14/12', u'boat=14/D', u'boat=15', u'boat=16', u'boat=2', u'boat=3', u'boat=4', u'boat=5', u'boat=5/7', u'boat=6', u'boat=7', u'boat=8', u'boat=9', u'boat=A', u'boat=B', u'boat=C', u'boat=D', u'home.dest=Aberdeen / Portland, OR', u'home.dest=Albany, NY', u'home.dest=Australia Fingal, ND', u'home.dest=Austria-Hungary / Germantown, Philadelphia, PA', u'home.dest=Ballydehob, Co Cork, Ireland New York, NY', u'home.dest=Bangkok, Thailand / Roseville, IL', u'home.dest=Barcelona, Spain / Havana, Cuba', u'home.dest=Bayside, Queens, NY', u'home.dest=Belgium  Montreal, PQ', u'home.dest=Belmont, MA', u'home.dest=Berne, Switzerland / Central City, IA', u'home.dest=Birkdale, England Cleveland, Ohio', u'home.dest=Bournmouth, England', u'home.dest=Bristol, Avon / Jacksonville, FL', u'home.dest=Brooklyn, NY', u'home.dest=Bryn Mawr, PA', u'home.dest=Calgary, AB', u'home.dest=Chelsea, London', u'home.dest=Chicago, IL', u'home.dest=Co Athlone, Ireland New York, NY', u'home.dest=Co Clare, Ireland Washington, DC', u'home.dest=Co Longford, Ireland New York, NY', u'home.dest=Cooperstown, NY', u'home.dest=Cornwall / Hancock, MI', u'home.dest=Deer Lodge, MT', u'home.dest=Denver, CO', u'home.dest=Detroit, MI', u'home.dest=Dowagiac, MI', u'home.dest=Duluth, MN', u'home.dest=England / Bennington, VT', u'home.dest=England Albion, NY', u'home.dest=England Brooklyn, NY', u'home.dest=England Oglesby, IL', u'home.dest=Finland / Minneapolis, MN', u'home.dest=Finland / Washington, DC', u'home.dest=Folkstone, Kent / New York, NY', u'home.dest=Green Bay, WI', u'home.dest=Greenwich, CT', u'home.dest=Guntur, India / Benton Harbour, MI', u'home.dest=Halifax, NS', u'home.dest=Harrisburg, PA', u'home.dest=Harrow, England', u'home.dest=Haverford, PA', u'home.dest=Haverford, PA / Cooperstown, NY', u'home.dest=Hessle, Yorks', u'home.dest=Hudson, NY', u'home.dest=India / Rapid City, SD', u'home.dest=Indianapolis, IN', u'home.dest=Italy Philadelphia, PA', u'home.dest=Lima, Peru', u'home.dest=Liverpool', u'home.dest=Liverpool, England Bedford, OH', u'home.dest=London  Vancouver, BC', u'home.dest=London /  East Orange, NJ', u'home.dest=London / Paris', u'home.dest=London, England Norfolk, VA', u'home.dest=Mt Airy, Philadelphia, PA', u'home.dest=New York, NY', u'home.dest=New York, NY / Ithaca, NY', u'home.dest=Norwich / New York, NY', u'home.dest=Paris / Haiti', u'home.dest=Paris, France', u'home.dest=Plymouth, Devon / Detroit, MI', u'home.dest=Rotherfield, Sussex, England Essex Co, MA', u'home.dest=Spain / Havana, Cuba', u'home.dest=St Louis, MO', u'home.dest=Sweden Winnipeg, MN', u'home.dest=Syria New York, NY', u'home.dest=Tuxedo Park, NY', u'home.dest=Wiltshire, England Niagara Falls, NY', u'home.dest=Youngstown, OH', u'room=B-18', u'room=B-49', u'room=C-101', u'room=C-125', u'room=C-7', u'room=D-35', u'ticket=111361 L57 19s 7d', u'ticket=13502 L77', u'ticket=17582 L153 9s 3d', u'ticket=17608 L262 7s 6d', u'ticket=230136 L39', u'ticket=24160 L221', u'ticket=392091'], dtype=object)\n",
      "41.2650346212\n",
      "embarked=Cherbourg\n"
     ]
    }
   ],
   "source": [
    "from sklearn import feature_selection\n",
    "fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=20)\n",
    "X_train_fs = fs.fit_transform(X_train, y_train)\n",
    "print titanic_data.columns[fs.get_support()]\n",
    "print fs.scores_[2]\n",
    "print titanic_data.columns[2]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Evaluate performance with the new feature set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.833 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "dt.fit(X_train_fs, y_train)\n",
    "X_test_fs = fs.transform(X_test)\n",
    "measure_performance(X_test_fs, y_test, dt, show_confussion_matrix=False, show_classification_report=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Find the best percentil using cross-validation on the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features:41 \n",
      "\n",
      "Mean scores: [ 0.83437791  0.88313996  0.8811095   0.87601782  0.87906868  0.88111468\n",
      "  0.87602818  0.88010981  0.88315032  0.8811095   0.88010981  0.87907386\n",
      "  0.87706413  0.87807417  0.8790894   0.87706413  0.87806899  0.87603336\n",
      "  0.87908422  0.8740029 ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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s7ERGjlQGjqjlq6+Uv/V//lNpFahMQYGIj4/IsmXqxXIjJo0aMBhEmjUTuXrVAgFVwa5d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\nzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x68ee3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import cross_validation\n",
    "\n",
    "percentiles = range(1, 100, 5)\n",
    "results = []\n",
    "for i in range(1, 100, 5):\n",
    "    fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=i)\n",
    "    X_train_fs = fs.fit_transform(X_train, y_train)\n",
    "    scores = cross_validation.cross_val_score(dt, X_train_fs, y_train, cv=5)\n",
    "    #print i,scores.mean()\n",
    "    results = np.append(results, scores.mean())\n",
    "\n",
    "optimal_percentil = np.where(results == results.max())[0]\n",
    "print \"Optimal number of features:{0}\".format(percentiles[optimal_percentil]), \"\\n\"\n",
    "\n",
    "# Plot number of features VS. cross-validation scores\n",
    "import pylab as pl\n",
    "pl.figure()\n",
    "pl.xlabel(\"Number of features selected\")\n",
    "pl.ylabel(\"Cross validation accuracy)\")\n",
    "pl.plot(percentiles,results)\n",
    "print \"Mean scores:\",results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Evaluate our best number of features on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.836 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs = feature_selection.SelectPercentile(feature_selection.chi2, percentile=percentiles[optimal_percentil])\n",
    "X_train_fs = fs.fit_transform(X_train, y_train)\n",
    "dt.fit(X_train_fs, y_train)\n",
    "X_test_fs = fs.transform(X_test)\n",
    "measure_performance(X_test_fs, y_test, dt, show_confussion_matrix=False, show_classification_report=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Model selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'splitter': 'best', 'min_density': None, 'compute_importances': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'random_state': None, 'criterion': 'entropy', 'max_features': None, 'max_depth': None}\n"
     ]
    }
   ],
   "source": [
    "print dt.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Compute the best criterion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entropy criterion accuracy on cv: 0.880\n",
      "Gini criterion accuracy on cv: 0.893\n"
     ]
    }
   ],
   "source": [
    "dt = tree.DecisionTreeClassifier(criterion='entropy')\n",
    "scores = cross_validation.cross_val_score(dt, X_train_fs, y_train, cv=5)\n",
    "print \"Entropy criterion accuracy on cv: {0:.3f}\".format(scores.mean())\n",
    "dt = tree.DecisionTreeClassifier(criterion='gini')\n",
    "scores = cross_validation.cross_val_score(dt, X_train_fs, y_train, cv=5)\n",
    "print \"Gini criterion accuracy on cv: {0:.3f}\".format(scores.mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:0.842 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "dt.fit(X_train_fs, y_train)\n",
    "X_test_fs = fs.transform(X_test)\n",
    "measure_performance(X_test_fs, y_test, dt, show_confussion_matrix=False, show_classification_report=False)"
   ]
  }
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